A Remote Estimation Method of Smart Meter Errors Based on Neural Network Filter and Generalized Damping Recursive Least Square

Abstract

To solve large-scale smart meter verification and periodic replacement problems, a remote estimation method based on neural network filter (NNF) and generalized damping recursive least square (GDRLS) is proposed. In this article, a smart meter error estimation model with a loss noise filter is built. A typical loss noise filter is designed with a neural network, so that the filtered loss noise meets the Gauss–Markov condition, which paves the way for the best linear unbiased estimation (BLUE). GDRLS algorithm is applied to solve the novel estimation model, which can effectively address the problem that large loss estimation errors merge small smart meter errors. Then, a complete process of the proposed method is constructed, which can estimate both the user smart meter errors, and the loss noises accurately. Finally, the effectiveness, superiority, and applicability of the proposed method are verified through simulation analysis and practical distribution network application.